نتایج جستجو برای: local outlier factor
تعداد نتایج: 1352534 فیلتر نتایج به سال:
Local adaptation to contrasting biotic or abiotic environments is an important evolutionary step that presumably precedes floral diversification at the species level, yet few studies have demonstrated the adaptive nature of intraspecific floral divergence in wild plant populations. We combine a population-genomic approach with phenotypic information on floral traits to examine whether the diffe...
Dear Editor, Dummy attack (DA), a deep stealthy but impactful data integrity on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting more practical case, we aim to detect one-shot DA, with purpose of revealing DA once it launched. Specifically, first formulate an optimization problem generate DAs. Then,...
Outlier is a data point that deviates too much from the rest of dataset. Most of real-world dataset have outlier. Outlier analysis is one of the techniques in data mining whose task is to discover the data which have an exceptional behavior compare to remaining dataset. Outlier detection plays an important role in data mining field. Outlier Detection is useful in many fields like Medical, Netwo...
Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods failure detection reducing safety risks from failing high energy lithium-ion batteries has become a major challenge industry. In this article, real-time scheme proposed. By applying both discrete Fréchet distance local outlier factor ...
In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is th...
Outlier detection is an important task in the field of data mining and a highly active area research machine learning. In industrial automation, datasets are often high-dimensional, meaning effort to study all dimensions directly leads sparsity, thus causing outliers be masked by noise effects high-dimensional spaces. The “curse dimensionality” phenomenon renders many conventional outlier metho...
We introduce a new nonparametric outlier detection method for linear series, which requires no missing or removed data imputation. For an arithmetic progression (a series without outliers) with n elements, the ratio (R) of the sum of the minimum and the maximum elements and the sum of all elements is always 2/n : (0,1]. R ≠ 2/n always implies the existence of outliers. Usually, R < 2/n implies ...
Data streams are ordered sets of values that are fast, continuous, mutable, and potentially unbounded. Examples of data streams include the pervasive time series which span domains such as finance, medicine, and transportation. Mining data streams require approaches that are efficient, adaptive, and scalable. For several stream mining tasks, knowledge of the data’s probability density function ...
In various industries, the process or product quality is evaluated by a functional relationship between dependent variable y and one few input variables x, expressed as y=fx. This called profile in literature. Recently, monitoring has received lot of research attention. this study, we formulated an anomaly-detection problem proposed outlier-detection procedure for phase I nonlinear analysis. Th...
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